 Today I'm going to I'm going to be talking about a bunch of different papers The title of the talk is the changing structure of Africa's economies The work I'm going to talk about today is Don't work with a bunch of different people one of them that Ken Hart can is here in the front row Danny Rodrick gonna go verduzco and Sebastian Volmer And I'd like to thank Diffit and ESRC for and the African Development Bank for financial support for this work so First I want to talk about motivation for the research then some recent evidence on structural change in Africa everything I'm going to talk about today's is is Africa base All the data are Africa only dated but Could be extended to other countries in region Then I'm going to talk about some recent work that I'm doing with Ken and Sebastian on Structural change using the DHS data demographic and health survey data and then summary and directions for work so the motivation for this work is The fact we all know that Africa has been growing relatively rapidly over the past decade or decade and a half and What we what we are less Aware of is what what exactly has been driving this growth? Is it high commodity prices? Is it structural change? Is it something else? So this graph What you what you should pay attention to are just the red green and blue solid lines. These are commodity prices for African countries it's a GDP weighted average and What you can see is in 2000 commodity prices started to skyrocket So there's a lot of speculation that Africa's recent growth is based on commodity prices alternatively There's there's there's the possibility that Africa's growth has actually been based on structural change. So what I have up here are There are three different panels agriculture industry and services The the original graphs are from a paper that was published in the QTE by Duarte, Margarita Duarte and Diego Rascuccia in 2010 and They look at structural change causes and consequences and implications of structural change in a cross-section of countries However, their data set includes no African countries So the innovation here is that we superimpose our data from Africa on the graph Sure share of agriculture and total employment and Right, so and and across the horizontal axis we have log GDP per capita So the red dots are Africa and the blue dots are the data from Diego Sorry Duarte and Rascuccia, so It's interest these I think these graphs are interesting for two reasons first of all they suggest that This is tangential to what I'm going to be talking about today But they suggest that Africa doesn't really look very different from the rest of the world given its current You know its levels of GDP per capita in 1990 in other words shares of employment and agriculture are very high in Africa But income per capita is also very low in Africa So it's what we would expect given income levels in Africa likewise for industry likewise for services, but but the important point as regards structural changes that in Africa most of the countries still have extraordinarily high shares of Employment in agriculture If we believe that productivity and output and consumption are lower in agriculture than they might be in Industry or services then there's huge scope for growth based on a movement of Employment out of agriculture and into other sectors of the economy So why should you care? Well understanding what's driving Africa's growth is important for understanding both the sustainability of the recent growth In other words, if it's just high commodity prices will the growth slow down once commodity prices fall again and also the likely distributional implications of the recent growth If there is significant structural change Then and in other words if we do see workers moving out of agriculture and into other sectors of the economies of these countries then the likelihood is that The is that incomes are rising for some of the poorest people in these countries so and In a paper I wrote with Danny Rodgers in 2011. We found that structural change in Africa for the period 1990 to 2005 had actually been growth reducing in other words There were actually people moving out of industry and back into agriculture I would like to take this opportunity To update you on those results because the update is very important so it turns out that when you When you break when you extend the sample to 2010 and actually I see my my colleague from the IMF in the in the audience and and He was very instrumental in helping me get the data to update these results. So I'd like to thank him but the left-hand side of the panel and the right-hand side of the panel are two different decades and What I did what I did is Still focus on only the countries in the Rodgers and McMillan sample but break out the The the data into two periods and I guess I lied because this includes not just Africa, but what I have is The blue line is with the insect or productivity growth and the red line is the productivity growth The red bar is the productivity growth that comes from moving say out of agriculture and into manufacturing where the marginal productivity of labor is higher So 1990 the 1999 you see Africa where Actually, if you if you take the average of the two for that decade The overall growth was slightly negative overall growth in GDP per capita was slightly negative and but but the more important and more Puzzling thing is that structural change contributed negatively to growth in Africa during that period However, if you look at the right-hand side You see that Between 2000 and 2010 there was positive structural change in Africa and almost half of the growth in Africa During that decade was a result of structural change in other words labor moving from low productivity activities to relatively higher productivity activities So why the change? Why the big change? I have a detailed paper addressing these issues But just to give you a sense during the 1990s many countries were still going through the throws of structural adjustment there's a paper by James Sirlo and Wolp's I don't remember his first name but that that talks about Zambia for example where the dismantling of the copper sector and The structural adjustment programs put into place led to deindustrialization and workers moving out of the city back to the countryside but that process Was more or less completed by the end of the 1990s and in the 2000s we see a renewed commitment to agriculture and increasing agricultural productivity You you are all probably aware of catap the common African agricultural development program Program Demographic trends rural population growth slowing down and political change. There's been a lot of political change not necessarily democracy as we know it but much more Expression of What people want to their governments? so but but But these country averages I mean sorry this this continent-wide average hides significant country-specific heterogeneity So for example, we have Mauritius here, which is a diversified economy this is showing that in Mauritius between the dates are up top 2000 and 2007 there were big changes in the economy of Mauritius But the changes in Mauritius were not characteristic of the changes in the rest of Africa the changes in Mauritius you saw The size of the circle represents the size of the sector in the economy and in Mauritius they have a Large manufacturing sector. That's much different from most other African countries And there was a there was a large as employment and manufacturing fell by six percentage points While employment in agriculture fell by around one and a half percentage points There was a huge increase in the share of employment in the services sector and that sector is large And also what the vertical what the vertical axis shows is that productivity in the services sector is Much higher in Mauritius than it is in manufacturing in agriculture. Why is that? That's because the services that Mauritius people engage in are tend to be high-tech and Techno sorry technology intensive and human capital intensive sorry On the other hand we have country like Nigeria that's some heavily resource dependent If you look along the horizontal axis the changes in employment shares are miniscule There's been some shift out of agriculture into manufacturing, but these are these are small The majority of the people still work in the agricultural sector you got it the Ugandan economy. This is um By the way, the titles are They probably remind you of those those categories used by McKinsey and company when they did them the emerging Africa report and They're roughly similar. So in Uganda you actually see pretty substantial structural change And even if you go back you see this so you see a large movement out of out of agriculture Primarily into services, but but also into manufacturing Malawi what we call a pre-transition economy the majority of the labor force still in agriculture very small changes in Malawi So summarizing the results from the macro data roughly half of Africa's recent growth can be attributed to structural change but the expansion in services Is only Sustainable for most countries if commodity prices remain high. I haven't showed you evidence of this But it's in a background paper high-skilled sir I believe that high-skilled services cannot at present be an engine of growth in Africa on a large scale because Educational levels if you look at the educational data, there's a new data set Well, it's not brand new but the data set that Barrow and Lee have it's publicly available on the internet all the way up to 2010 education levels in Africa are still extremely extremely low So the kinds of services African people the majority of African workers can enter into at this point are not High very high productivity high paying kinds of jobs Manufacturing has a lot of potential, but it's still lagging and I just want to point you to To a paper that Jim Robinson recently wrote on the possibility of using natural resources for industrialization It's hopeful and It it and it looks at history the Industrial Revolution in the UK for example and explains how it was that natural resources Fostered industrialization, so there's a lot of potential But there as as as we know there are serious limitations to using macro data So there are differences in the treatment of informality across countries So Kenya is is is one example if you look at the WDI or Aggregate statistics for Kenya You'll see that there's a teeny tiny share of the labor force in manufacturing But if you know about the Juwakali in Kenya, you know that that there's something wrong with those statistics And indeed the government of Kenya separates out the formal sector from the informal sector So if you just take one macro survey and look at those numbers the results are very misleading Then there are differences in the treatment of agriculture across countries So even with and within countries so in Botswana, which has some of the best data in Africa We found that they did the agricultural census in two different years in the labor force survey during two different time periods And didn't take that into account so that it shows a huge increase in the share of the labor force working in agriculture when the real reason for that is because One survey was taken during the lean season and one survey was taken during the the Period during which more people are employed in agriculture. So there are problems with the macro data and Also, there are limited shares. There's limited information on Employment shares. So even in the world development indicators, there's hardly anything On the share of employment in I think there may be six countries for Africa for which there exists data. So Employment share, I mean and that's even for three broad sectors. So if you want to get at a more detailed analysis of employment shares The data are lagging. I just wanted Never mind. So but even if national accounts data are perfect the macro data ignore the following So there's important within country heterogeneity, for example across age groups across gender across education and across geographic location and the the national income accounts only measure one standard of welfare, which is income or consumption. Thanks. So What we're doing now the most exciting part in which I only have five minutes to tell you about is using the demographic and health surveys to Understand to learn what we can based on those data about structural change in Africa and One of the nicest things about the DHS data is if you look at Africa The the darker the the country the more survey rounds we have But almost all of sub-Saharan Africa is covered at least once and several countries are covered twice If you if you focus so so I'm just going to show you now some The rest of the time I'm going to show you some summary statistics and some regressions that are more or less summary statistics based on these DHS data So forget about the first set of bar graphs If you look at the second set of bar graphs what we've done is we're looking at individual level data So we have women and men and they're asked about their sector of employment They're asked whether they work seasonally. They're asked whether they've done this job in the past 12 months and so what you see between 93 2004 and 2005 and 2011 is actually very consistent with what we found with the macro data the share of the labor force in agriculture if you include Self-employed agriculture and an employee in agriculture in other words you you merge the red and the yellow bars You see that the share of employment in agriculture has fallen but not Dramatically One thing we have here is the share of people reporting that they're not working. It looks high. I know we've excluded Everybody enrolled in school. It's people ages 15 to 59 and The share of the labor force reporting that they're not working that's falling from 30 cent to 20 30 percent to 26 percent And the share of employees and skilled and unskilled manual labor has Risen from 9% to about 11% so these figures on average are very consistent with what we found with the macro data But we have a lot more countries so for the for the middle period We have 45 countries in Sub-Saharan Africa and for the last country period we have 26 countries in Sub-Saharan Africa now if we look at What I'm going to show you now is Just a couple of regressions really quickly. They're all the same The only thing that's going to differ across these next five sides is the sample So here's the full sample. It's more or less what we would expect So if you don't have an education, you're more likely to work in agriculture You're less likely to work in all these other professional sales and skilled jobs And you're more likely to be not working the youth The big thing about youth is if you go to the last column youth are 13% more likely to report that they're not working and That's not including people who are enrolled in school for a second time We've excluded people who are enrolled in school in urban areas You're 35.9% much less likely to report that you're working in agriculture all the numbers are consistent with Luckily, they make sense, you know, we don't expect urban people to be as much engaged in agriculture as the rural and Not working you're 4% more likely to be not working if you're in the Rural areas and then for female females are 16% less likely to be working in agriculture all 18.5% more likely to be not working finally The the last two rows are GDP per capita and the polity for score so a general measure of governance in general our general measure of governance Has the right sign, but it's very small and sometimes insignificant GDP per capita. On the other hand has big effects the In general GDP per capita leads to people reporting. They're working more in every sector and they're less likely to be unemployed Let me show you men and women. There are different very very different effects Across males and females. So this is This is actually really interesting here and the reason I included GDP per capita in here one of the reasons is because We want to talk about it. This is a conference about inclusive growth. So look at that GDP per capita leads to Increases in GDP per capita lead to 14.2% Reduction in the probability that a woman is not working. So increases in growth lead to better Not better, but more women working Interestingly, it's the reverse for men. It's mostly it's primarily an urban phenomenon and a youth phenomenon because if you look at men who Are in rural areas. I'm done. I'm done Okay, I like this Look at this. This GDP per capita has a much bigger effect in rural areas than in urban areas interesting and Oh shit Yeah, yeah, okay, so that that's it. So preliminary results from the DHS data We obviously have a lot more work to do but the broad patterns are consistent with the macro data Women are much less likely to be more likely to be unemployed and much less likely to be employed in agriculture Both it appears to be inclusive in so much as it has quantitatively larger effects on Employment in rural areas, which is kind of interesting and only caveat is though is that it could be increasing to rural urban migration youth are much more likely to be unemployed and youth in urban areas youth female youth in urban areas are the worst and We have a lot more to do the next step is to Look at we want to look at health and education by sector so that we can get Some indicators of welfare by sector since we have all these different sectors So to look at you know instead of looking at TFP for example by sector We want to look at other indicators of well-being by sector to better interpret what the changes in employment across sectors means For Africa. Thank you very much